Long-term exposure to transportation noise and obesity: A pooled analysis of eleven Nordic cohorts

Background: Available evidence suggests a link between exposure to transportation noise and an increased risk of obesity. We aimed to assess exposure-response functions for long-term residential exposure to road traffic, railway and aircraft noise, and markers of obesity. Methods: Our cross-sectional study is based on pooled data from 11 Nordic cohorts, including up to 162,639 individuals with either measured (69.2%) or self-reported obesity data. Residential exposure to transportation noise was estimated as a time-weighted average Lden 5 years before recruitment. Adjusted linear and logistic regression models were fitted to assess beta coefficients and odds ratios (OR) with 95% confidence intervals (CI) for body mass index, overweight, and obesity, as well as for waist circumference and central obesity. Furthermore, natural splines were fitted to assess the shape of the exposure-response functions. Results: For road traffic noise, the OR for obesity was 1.06 (95% CI = 1.03, 1.08) and for central obesity 1.03 (95% CI = 1.01, 1.05) per 10 dB Lden. Thresholds were observed at around 50–55 and 55–60 dB Lden, respectively, above which there was an approximate 10% risk increase per 10 dB Lden increment for both outcomes. However, linear associations only occurred in participants with measured obesity markers and were strongly influenced by the largest cohort. Similar risk estimates as for road traffic noise were found for railway noise, with no clear thresholds. For aircraft noise, results were uncertain due to the low number of exposed participants. Conclusion: Our results support an association between road traffic and railway noise and obesity.


Long-term exposure to transportation noise and obesity -A pooled analysis of eleven Nordic cohorts
Åsa Persson et al.
Table S1: Detailed information on the participating cohorts and outcome assessment method.....

PPS
The Primary Prevention Study cohort (PPS) consists of a random third of all men in the city of Gothenburg born 1915Gothenburg born -1925Gothenburg born , recruited in 1970Gothenburg born -1973 (n=7,495, participation (n=7,495, participation  The Bank of Sweden Tercentenary Fund and the Swedish Medical Research Council.

GOT-MONICA
The GOT-MONICA included a total of 2,339 men and 2,536 women recruited as a random selection of residents in Gothenburg aged 25-64 years in 1990, and 1995.The participants filled out questionnaires on e.g.

FINRISK
The national FINRISK study is a set of large population surveys that have been conducted to monitor non-communicable disease risk factors, health behavior and their changes in the population.Every five years during 1972-2012, a stratified random sample has been selected from the 25-74 (64 in earlier surveys) year old inhabitants in different regions of Finland.

SMC
Exposure to road traffic noise was assessed using the Nordic Prediction Method (Bendtsen 1999).Input data included ground surface (assuming flat terrain), road net and traffic flows (simulated and calibrated against historical measurements) on both state owned and municipal roads, diurnal distributions, percentage heavy vehicles, speed, and buildings.The exposure was calculated as free-field levels at the façade of the buildings at 2 m height.Within urban areas (primarily within the city of Uppsala), the calculations were performed with second-order reflections, whereas in more rural areas, first-order of reflections were used.The search radii were set to 1,000 m within Uppsala and to 1,500 m in more rural areas.The estimated sound parameter, Lden, was calculated for each address every fifth year from 1990 to 2015.To calculate yearly averages, linear interpolation was applied. 1990applied. , 1995applied. , 2000applied. , 2005applied. , 2010applied. , 2015applied. Bendtsen, H., 1999 Yearly traffic counts (separate for day, evening and night), train speed and composition of different train types were obtained from the environmental office of the municipality of Gothenburg and the traffic office of the municipality of Mölndal for trams.For standard rail traffic, the same information was obtained from the Swedish traffic administration.Before 1997 regional and national rail transport statistics and published timetables were used to estimate traffic counts.Noise barriers of at least 2 m height and 100 m length were also included, as well as earth berms in the terrain model.To save calculation time and reduce demands on detailed input data a simplified methodology was used for multiple reflections in dense urban areas.All receivers were assumed to be at 2 m height above ground at the address coordinate.

Key references
SDPP, SIXTY, SNAC-K, SALT Estimations were based on a noise database for Stockholm County which represented the period from 2010 and retrospectively until 1997, with detailed annual estimates.The database includes 3D terrain data as well as information on ground surface, railway net, speed limits and annual average daily train flows separately for light and heavy trains.To calculate noise levels for railway traffic a modification of the Nordic prediction method was used, where possible reflection and shielding were taken into account by a Ground Space Index based on building density.The methodology has been adopted and further developed from the one for road traffic noise described by Ögren and Barregard (2016), which was validated against the full Nordic prediction method modelled with SoundPlan and showed coherent estimates.Level day-evening-night was estimated from the equivalent level using an adjustment of 6 dB.Table S3: Detailed information on estimation of air pollution for the participating cohorts.

DCH
In the DCH cohort, we used the DEHM-UBM-AirGIS modelling system to calculate PM2.5 and NO2 at all Danish addresses for the years 2000, 2010 and 2015, which was then extrapolated to yearly means for each address, based on changes in yearly urban background levels.This multi-scale dispersion modelling system calculates air pollutants at each address as the sum of a) PM2.5/NO2 from the nearest street, calculated based on traffic, car fleet emission factors, streets and building geometry, and meteorology; b) urban background, calculated based on city dimensions, emission density, and heights of buildings; and c) regional background, calculated based on all emissions in the northern hemisphere.

MDC
Air pollutants (PM2.5, and nitrogen oxides converted to NO2) were modelled using EnviMan (Opsis AB, Sweden) by the Environmental Department, City of Malmö, using a Gaussian dispersion model (AERMOD) combined with an emission database for the county of Scania in Sweden.The 18 × 18 km modelling area covered the city of Malmö and the closest surroundings.Separate emission databases were compiled for 1992, 2000 and 2011 using existing local and regional bottom-up inventories provided by the municipality, and then supplemented to be consistent for the whole area and time-period.Yearly mean concentrations were stored as grids with a spatial resolution of 50 m × 50 m.The years in between the modelled years were interpolated linearly with adjustment for yearto-year variations in the local meteorology using a ventilation factor estimated from calculations over the whole time-period, and exposure for the years 1990 and 1991 extrapolated.Exposure data was combined with geocoded addresses to assign each participant annual residential exposures. Hasslöf

GOT-MONICA
Same method as for PPS.

SDPP, SIXTY, SNAC-K, SALT
In the Stockholm County cohorts, a high-resolution Gaussian dispersion model was used to estimate individual residential levels of PM2.5 and NOx/NO2 using local emission inventories every fifth year from 1990 and onwards.The emission inventory contains detailed information on local emissions from road and ferry traffic, industrial areas and households.Meteorological input to the modelling includes measurements of wind velocity and direction, solar radiation and temperature.Further, a street canyon contribution is added for addresses in the most polluted street segments of the inner city of Stockholm with multi-storey houses on both sides.Annual averaged long-range contributions were added to the locally modelled concentrations based on continuous measurements at regional background monitoring stations.
Segersson D, Eneroth K, Gidhagen L, et al.FINRISK Estimates of PM2.5 and NO2 concentrations were based on dispersion modelling using models developed in the Finnish Meteorological Institute.The calculations included emissions from energy production, industry, ship traffic and road traffic.The average measured background was added to the concentrations.Emissions used in the calculations represented the situation in Turku Region in 2007 and 2014 in the Helsinki Capital Region.In the dispersion models, the distances between the receptor points varied from 25 m near the roads to 500 m in rural areas.In the present study, the modelled annual average concentration at the nearest outdoor receptor point was used as a proxy of home outdoor concentration., 1995-2000, >2000), educational level, marital status, area income, smoking status, and physical activity.Analytical samples differ for analysis with different interactors for road traffic and railway noise.Overweight: Body Mass Index ≥25 kg/m²., 1995-2000, >2000), educational level, marital status, area income, smoking status, and physical activity.Analytical samples differ for analysis with different interactors for road traffic and railway noise.Central obesity: Waist circumference in women: ≥88 cm, and in men: ≥102 cm.

Figure S1 :
Figure S1: Histograms of road traffic, railway, and aircraft noise exposure for the pooled cohort (displaying levels of ≥40 dB Lden only).

Figure S2 :
Figure S2: Cohort-specific histograms of exposure to road traffic noise (displaying levels of ≥40 dB Lden only).

Figure S3 :
Figure S3: Cohort-specific histograms of exposure to railway noise (displaying levels of ≥40 dB Lden only).

Figure S4 :
Figure S4: Cohort-specific histograms of exposure to aircraft noise (displaying levels of ≥40 dB Lden only).Data only available in 5 dB for DCH, DNC and FINRISK.

Figure S8 :
Figure S8: Overweight in relation to road traffic and railway noise exposure 5 years prior to baseline (OR and 95% CI per 10 dB Lden) according to different characteristics of the study subjects.

Figure S9 :
Figure S9: Central obesity in relation to road traffic and railway noise exposure 5 years prior to baseline (OR and 95% CI per 10 dB Lden) according to different characteristics of the study subjects.

Figure S10 :
Figure S10: Exposure-response associations between road traffic noise and overweight, obesity and central obesity, respectively, based on cohorts with measured outcomes only and additionally excluding the DCH in the lower panels.Measured overweight

Figure S11 :
Figure S11: Cohort-specific results performed through interaction analyses for overweight, obesity and central obesity in relation to road traffic noise (OR and 95% CI per 10 dB Lden increase).

Figure S12 :
Figure S12: Cohort-specific results performed through interaction analyses for overweight, obesity and central obesity in relation to railway noise (OR and 95% CI per 10 dB Lden increase).

Table S2 :
Detailed information on estimation of road traffic, railway, and aircraft noise for the participating cohorts.DCHCalculations were conducted using the Nordic prediction method implemented in SoundPLAN (version 8.0).Various input variables were used in the model, most importantly geocode and height (floor) for each address, information on travel speed, light/heavy vehicle distributions, road type, annual average daily traffic for all Danish road links (Jensen et al 2019) and 3D information on all Danish buildings.Screening effects from buildings, terrain, and noise barriers were included.All road traffic sources within 1500 m from the receivers were included.The parameters were set to allow 2 reflections.-term individual transportation noise exposure a noise database for Stockholm County was developed representing the period from 1990 and onwards, with detailed estimation every fifth year.The database includes 3D terrain data as well as information on ground surface, road net, daily traffic flows (≥1000 vehicles/day), speed limits and percentage of heavy vehicles.To calculate noise levels for road traffic a modification of the Nordic prediction method was used, where possible reflection and shielding were taken into account by a Ground Space Index based on building density.The methodology has been further developed from the one described by Ögren and Barregard (2016), which was validated against the full Nordic prediction method modelled with SoundPlan and showed coherent estimates.
Jensen SS, Plejdrup MS, Hillig K.GIS-based National  Road and Traffic Database 1960-2020.AarhusUniversity, Danish Centre for Environment and Energy 2019; Report 151.DNC Same method as for DCH.MDC Estimated using the Nordic Prediction Method implemented in SoundPLAN (version 8.0, SoundPLAN Nord ApS).Input variables included geocode, information on annual average daily traffic for all road links in Malmö municipality, distribution of light/heavy traffic, signposted travel speed and road type and polygons for all buildings in Malmö.All road traffic sources within 1000 m from the receivers were included.Traffic data were retrieved from a regional emission database (Rittner et al. 2020).The screening effects from buildings were included and ground softness considered.The parameters in the models were set to allow 2 reflections and receivers placed at 2 m height.Estimation of road traffic noise exposure between years with models was based on the model closest in time or the year of major changes in infrastructure, i.e. the model from 1990 was used for residential coordinates 1985-1999, the model from 2000 used for coordinates 2000-2005 and the model from 2010 for coordinates between 2006 and 2016.1990, 2000, 2010 Bendtsen, H., 1999.The nordic prediction method for road traffic noise.Sci.Total Environ.235, 331-338.https://doi.org/10.1016/S0048-9697(99)00216-8.
Façade noise levels from road-traffic were calculated by consulting companies in accordance with the EU Environmental Noise Directive 2002/49/EC50 using input data for the year 2011.The Nordic Prediction Method was used for major highways, main streets, and collector streets within Helsinki, Vantaa, and Turku.Input variables included terrain elevation data, traffic flows on the road network, speed limits and percentage of heavy vehicles, locations and heights of noise barriers.In addition, bridges, road profiles as well as acoustic hardness of terrain or water surfaces were specified.All road traffic sources within 2000 m from the receivers were included.Counting height was 4 m.The parameter setting were set to allow 1 reflection, but not from the facade in question.No Various input variables were used in the model, most importantly geocode and height (floor) for each address, information on train speed and type, annual average trains for the day, evening and night and 3D information on all Danish buildings within 1000 m of the railway.Screening effects from buildings, terrain, and noise barriers were included.All rail traffic sources within 1500 m from the receivers were included.The parameters were set to allow 2 reflections.Nordic Prediction Method implemented in SoundPLAN (version 8.0, SoundPLAN Nord ApS).Input variables included geocode, information on annual average daily traffic and speed for all railways, train types and polygons for all buildings in Malmö.All railway traffic sources within 1000 m from the receivers were included.Screening effects from buildings were included and ground softness was considered.The parameters in the models were set to allow 2 reflections and receivers placed at 2 m height.Estimation of railway noise exposure between years with models was based on the model closest in time or the year of major changes in infrastructure.Level day-eveningnight was estimated from the equivalent level using an adjustment of 6 dB.
. The nordic prediction method for road traffic noise.Sci.Total Environ.235, 331-338.https://doi.org/10.1016/S0048-9697(99)00216-8.FINRISK Segersson et al. 2017).Sources considered were emissions from road traffic (including street canyon effect where applicable), boilers and energy plants, individual heating with solid fuel (wood) and oil, shipping, and long-range transport.Yearly average concentrations of PM2.5, PM10 and NO2 for all sources combined were calculated for all addresses of our study participants.

Table S5 :
Pooled and cohort-specific results for aircraft noise.Not applicable; BMI, Body Mass Index; DCH, Diet, Cancer and Health cohort; DNC, Danish Nurses Cohort; GOT-MONICA, Multinational Monitoring of Trends and Determinants in Cardiovascular Disease cohort (Gothenburg); MDC, Malmö Diet and Cancer Study; PPS, Primary Prevention Study cohort; SALT, Stockholm Screening Across the Lifespan Twin Study; SDPP, Stockholm Diabetes Prevention Programme; Sixty, the Stockholm Cohort of 60-year-olds; SMC, The Swedish Mammography Cohort; SNAC-K, Swedish National Study of Aging and Care in Kungsholmen.All estimates are expressed as per 10 dB Lden increase.Adjusted for cohort, sex, age, recruitment year, educational level, marital status, area income, smoking status and physical activity.

Table S6 :
Sensitivity analyses for road traffic noise in relation to overweight, obesity and central obesity (OR and 95% CI per 10 dB Lden increase).Model 2, main model, adjusted for age, sex, recruitment year, cohort, educational level, marital status, smoking status, physical activity and area level income.

Table S7 :
Sensitivity analyses for railway noise in relation to overweight, obesity and central obesity (OR and 95% CI per 10 dB Lden increase).Body Mass Index ≥25 kg/m² vs Body Mass Index <25 kg/m² as reference category.b Obesity: Body Mass Index ≥30 kg/m² vs Body Mass Index <25 kg/m² as reference category.Model 2, main model, adjusted for age, sex, recruitment year, cohort, educational level, marital status, smoking status, physical activity and area level income.e Cohorts: DCH, GOT-Monica, PPS, MDC, SDPP, SNAC-K, SIXTY, and FINRISK.